Retrieval-Constrained Valuation: Toward Prediction of Open-Ended Decisions

Real-world decisions are often open-ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here we take a step toward addressing this issue by developing a neurally-inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation, or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together these results provide a neurally-grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.

Zhang et al, Proceedings of the National Academy of Sciences, 2021